NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Performance evaluation of the photovoltaic system

The various renewable energy source technologies, Photovoltaics (PV) transforming sunlight directly into electricity, have become standard practice worldwide, especially in countries with high solar radiation levels. PV systems have been developed rapidly over recent years, and many new technologies have emerged from different producers. For each type of PV module, manufacturers provide specific information on rated performance parameters, including power at maximum power point (MPP), efficiency and temperature factors, all under standard solar test conditions (STC) 1000 W/m2. Air. In addition, the mass (AM) of 1.5 and the cell's temperature was 25 ̊C. Unfortunately, this grouping of environmental conditions is infrequently found in outdoor conditions. Also, the data provided by the manufacturers are not sufficient to accurately predict the performance of photovoltaic systems in various climatic conditions. Therefore, monitoring and evaluating the performance of the off-site systems is necessary. This thesis aims to overview various photovoltaic technologies, ranging from crystalline silicon (c-SI) to thin-film CdTe and GiCs. The following are the main parameters for evaluating the external units' performance to describe the PV systems' operation and implementation. In addition, a review of the impacts of various environmental and operational factors, such as solar radiation, temperature, spectrum, and degradation

Machine learning for advanced characterisation of silicon solar cells

Improving the efficiency, reliability, and durability of photovoltaic cells and modules is key to accelerating the transition towards a carbon-free society. With tens of millions of solar cells manufactured every day, this thesis aims to leverage the available characterisation data to identify defects in solar cells using powerful machine learning techniques. Firstly, it explores temperature and injection dependent lifetime data to characterise bulk defects in silicon solar cells. Machine learning algorithms were trained to model the recombination statistics’ inverse function and predict the defect parameters. The proposed image representation of lifetime data and access to powerful deep learning techniques surpasses traditional defect parameter extraction techniques and enables the extraction of temperature dependent defect parameters. Secondly, it makes use of end-of-line current-voltage measurements and luminescence images to demonstrate how luminescence imaging can satisfy the needs of end-of-line binning. By introducing a deep learning framework, the cell efficiency is correlated to the luminescence image and shows that a luminescence-based binning does not impact the mismatch losses of the fabricated modules while having a greater capability of detecting defects in solar cells. The framework is shown in multiple transfer learning and fine-tuning applications such as half-cut and shingled cells. The method is then extended for automated efficiency-loss analysis, where a new deep learning framework identifies the defective regions in the luminescence image and their impact on the overall cell efficiency. Finally, it presents a machine learning algorithm to model the relationship between input process parameters and output efficiency to identify the recipe for achieving the highest solar cell efficiency with the help of a genetic algorithm optimiser. The development of machine learning-powered characterisation truly unlocks new insight and brings the photovoltaic industry to the next level, making the most of the available data to accelerate the rate of improvement of solar cell and module efficiency while identifying the potential defects impacting their reliability and durability

NASA Small Business Innovation Research Program. Composite List of Projects, 1983 to 1989

The NASA SBIR Composite List of Projects, 1983 to 1989, includes all projects that have been selected for support by the Small Business Innovation Research (SBIR) Program of NASA. The list describes 1232 Phase 1 and 510 Phase 2 contracts that had been awarded or were in negotiation for award in August 1990. The main body is organized alphabetically by name of the small businesses. Four indexes cross-reference the list. The objective of this listing is to provide information about the SBIR program to anyone concerned with NASA research and development activities

Automated control of laser systems for micromachining

In this thesis, the effects of process parameters on the resulting feature morphology and dimensions within line length scales and micro-fluidic devices is presented. Positioning stages, laser systems, and autonomous control systems were developed and designed for the machining of micro-channels on glass sheet and inside polycarbonate and PMMA samples. The developed real time closed loop control system was set-up via reconfigurable I/O Field-Programmable Gate Array (FPGA). In-depth analyses of the positional performance of the developed Nd:YVO4, and Nd:YAG laser systems were carried out. The results of these analyses indicated that the developed 3D translation stage of the Nd:YVO4 laser system is better with accuracy and repeatability values less than 65 µm for all the three axes. In particular, CO2 (1.5 kW, 10.6 µm) and Nd:YVO4 (2.5 W, 1.064 µm) laser systems were investigated experimentally and through system models in order to better understand the effects of laser and motion parameters on the process control. Predictive models, that relate the laser machining parameters (laser power; P, pulse repetition frequency; PRF, and sample translation speed; U) to the geometry and cost of the produced micro-channels, were developed. Detailed designs of experiments (DoE) were conducted and results from developed predictive models based on Artificial Neural Network (ANN) and Response Surface Methodology (RSM) techniques were compared with the actual results. Statistical estimators were used to evaluate these models and compare their predictive and generalization ability. Results showed that although the ANN models provided the highest prediction accuracy, both RSM and ANN modelling techniques could be utilised as effective predictive tools for resultant laser micro-machined dimensions and selection of laser micromachining parameters

Microgravity Science and Applications: Program Tasks and Bibliography for Fiscal Year 1996

NASA's Microgravity Science and Applications Division (MSAD) sponsors a program that expands the use of space as a laboratory for the study of important physical, chemical, and biochemical processes. The primary objective of the program is to broaden the value and capabilities of human presence in space by exploiting the unique characteristics of the space environment for research. However, since flight opportunities are rare and flight research development is expensive, a vigorous ground-based research program, from which only the best experiments evolve, is critical to the continuing strength of the program. The microgravity environment affords unique characteristics that allow the investigation of phenomena and processes that are difficult or impossible to study an Earth. The ability to control gravitational effects such as buoyancy driven convection, sedimentation, and hydrostatic pressures make it possible to isolate phenomena and make measurements that have significantly greater accuracy than can be achieved in normal gravity. Space flight gives scientists the opportunity to study the fundamental states of physical matter-solids, liquids and gasses-and the forces that affect those states. Because the orbital environment allows the treatment of gravity as a variable, research in microgravity leads to a greater fundamental understanding of the influence of gravity on the world around us. With appropriate emphasis, the results of space experiments lead to both knowledge and technological advances that have direct applications on Earth. Microgravity research also provides the practical knowledge essential to the development of future space systems. The Office of Life and Microgravity Sciences and Applications (OLMSA) is responsible for planning and executing research stimulated by the Agency's broad scientific goals. OLMSA's Microgravity Science and Applications Division (MSAD) is responsible for guiding and focusing a comprehensive program, and currently manages its research and development tasks through five major scientific areas: biotechnology, combustion science, fluid physics, fundamental physics, and materials science. FY 1996 was an important year for MSAD. NASA continued to build a solid research community for the coming space station era. During FY 1996, the NASA Microgravity Research Program continued investigations selected from the 1994 combustion science, fluid physics, and materials science NRAS. MSAD also released a NASA Research Announcement in microgravity biotechnology, with more than 130 proposals received in response. Selection of research for funding is expected in early 1997. The principal investigators chosen from these NRAs will form the core of the MSAD research program at the beginning of the space station era. The third United States Microgravity Payload (USMP-3) and the Life and Microgravity Spacelab (LMS) missions yielded a wealth of microgravity data in FY 1996. The USMP-3 mission included a fluids facility and three solidification furnaces, each designed to examine a different type of crystal growth

Synthesis, optimisation and control of crystallization systems

Process systems engineering has provided with a range of powerful tools to chemical engineers for synthesis, optimisation and control using thorough understanding of the processes enhanced with the aid of sophisticated and accurate multi-faceted mathematical models. Crystallization processes have rarely benefited from these new techniques, for they lack in models that could be used to bridge the gaps in their perception before utilising the resulting insight for the three above mentioned tasks. In the present work, first a consistent and sufficiently complex models for unit operations including MSMPR crystallizer, hydrocyclone and fines dissolver are developed to enhance the understanding of systems comprising these units. This insight is then utilised for devising innovative techniques to synthesise, optimise and control such processes. A constructive targeting approach is developed for innovative synthesis of stage-wise crystallization processes. The resulting solution surpasses the performance obtained from conventional design procedure not only because optimal temperature profiles are used along the crystallizers but also the distribution of feed and product removal is optimally determined through non-linear programming. The revised Machine Learning methodology presented here for continual process improvement by analysing process data and representing the findings as zone of best average performance, has directly utilised the models to generate the data in the absence of real plant data. The methodology which is demonstrated through KNO₃ crystallization process flowsheet quickly identifies three opportunities each representing an increase of 12% on nominal operation. An optimal multi-variable controller has been designed for a one litre continuous recycle crystallizer to indirectly control total number and average size of crystals from secondary process measurements. The system identification is solely based on experimental findings. Linear Quadratic Gaussian method based design procedure is developed to design the controller which not only shows excellent set-point tracking capabilities but also effectively rejects disturbance in the simulated closed loop runs

Microgravity Science and Applications Program tasks, 1987 revision

A compilation is presented of the active research tasks as of the end of the FY87 of the Microgravity Science and Applications Program, NASA-Office of Space Science and Applications, involving several NASA centers and other organizations. An overview is provided of the program scope for managers and scientists in industry, university, and government communities. An introductory description is provided of the program along with the strategy and overall goal, identification of the organizational structures and people involved, and a description of each task. A list of recent publications is also provided. The tasks are grouped into six major categories: Electronic Materials; Solidification of Metals, Alloys, and Composites; Fluid Dynamics and Transport Phenomena; Biotechnology; Glasses and Ceramics; and Combustion. Other categories include Experimental Technology, General Studies and Surveys; Foreign Government Affiliations; Industrial Affiliations; and Physics and Chemistry Experiments (PACE). The tasks are divided into ground based and flight experiments